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In order to further improve the accuracy of photovoltaic (PV) power prediction and the stability of power system, a short-term PV power prediction model based on hierarchical clustering of K-means++ algorithm and deep learning hybrid model is proposed in this paper. First, hierarchical clustering of the K-means++ algorithm is used to cluster historical data into different weather scenes according to different seasons. Second, a hybrid model combining convolutional neural network (CNN), squeeze-and-excitation attention mechanism (SEAM), and bidirectional long short-term memory (BILSTM) neural network is constructed to capture long-term dependencies in time series, and the improved pelican optimization algorithm (IPOA) is used to optimize the hyperparameters in the prediction model. Finally, an example for modeling analysis is conducted by using the actual output and meteorological data of a PV power station in the Ili region of Xinjiang, China. The effectiveness and accuracy of the proposed model are verified by comparing with LSTM, BILSTM, CNN-BILSTM, and POA-CNN-SEAM-BILSTM models, and the superiority of IPOA is verified by comparing with particle swarm optimization and whale optimization algorithm. The results show that the proposed model can obtain better results under different weather scenes in different seasons, and the prediction accuracy of the model optimized by IPOA is further improved. [ABSTRACT FROM AUTHOR]

For deep learning's insufficient learning ability of a small amount of data in the Chinese named entity recognition based on deep learning, this paper proposes a named entity recognition of local adverse drug reactions based on Adversarial Transfer Learning, and constructs a neural network model ASAIBC consisting of Adversarial Transfer Learning, Self-Attention, independently recurrent neural network (IndRNN), Bi-directional long short-term memory (BiLSTM) and conditional random field (CRF). However, of the task of Chinese named entity recognition (NER), there are only few open labeled data sets. Therefore, this article introduces Adversarial Transfer Learning network to fully utilize the boundary of Chinese word segmentation tasks (CWS) and NER tasks for information sharing. Plus, the specific information in the CWS is also filtered. Combing with Self-Attention mechanism and IndRNN, this feature's expression ability is enhanced, thus allowing the model to concern the important information of different entities from different levels. Along with better capture of the dependence relations of long sentences, the recognition ability of the model is further strengthened. As all the results gained from WeiBoNER and MSRA data sets by ASAIBC model are better than traditional algorithms, this paper conducts an experiment on the data set of Xinjiang local named entity recognition of adverse drug reactions (XJADRNER) based on manual labeling, with the accuracy, precision, recall and F-Score value being 98.97%, 91.01%, 90.21% and 90.57% respectively. These experimental results have shown that ASAIBC model can significantly improve the NER performance of local adverse drug reactions in Xinjiang. [ABSTRACT FROM AUTHOR]

Power stations operating on PhotoVoltaic (PV) power generation are challenged by demand forecasting as PV power generation is random and intermittent. This paper presents an Efficient Shrinkage Temporal Convolutional Network (ESTCN) model, which combines the Temporal Convolutional Network (TCN) and an improved Deep Residual Shrinkage Network (DRSN) to forecast PV power output. First, the attention sub-network of the DRSN is improved, the fully connected layer of the sub-network is canceled, and feature extraction is done directly on the model features following global average pooling using a one-dimensional convolution to obtain cross-channel interaction and increased model efficiency. Next, an improved attention mechanism and adaptive soft thresholding are introduced into TCN to automatically determine the noise threshold to address the issue of information weight dispersion caused by redundant information in the input samples. By incorporating dilated causal convolution, attention module, soft thresholding, and residual connection, the ESTCN model is formed and is shown to enhance PV power output prediction with only a minimal increase in the number of parameters. The power station in Ningxia, China is adopted as a validation example with data taken from the year 2020. The fitting and prediction results of the ESTCN model are compared against the Convolutional Neural Network, Long Short-Term Memory, and TCN models. The ESTCN model yielded the following values of the evaluation metrics: Root Mean Square Error (RMSE) of 1.47 kW, Mean Absolute Error (MAE) of 0.79 kW, and coefficient of determination of 0.999. Compared to the TCN model, the prediction errors based on RMSE and MAE improved by 0.39 kW and 0.18 kW, respectively. Applying the ESTCN model to predict PV power generation of power stations in two regions in China, Ningxia and Xinjiang, shows the ESTCN model to be superior, scalable, and universal over other deep learning models. • Study the problem of photovoltaic power using a deep learning method. • A new Efficient Shrinkage Temporal Convolutional Network model is presented. • Attention sub-network of DRSN is improved by cancelling the fully connected layer. • Feature extraction is done on the model features using a one-dimensional convolution. • Improved attention mechanism and adaptive soft thresholding are introduced into TCN. [ABSTRACT FROM AUTHOR]

Agricultural fields are essential in providing human beings with paramount food and other materials. Quick and accurate identification of agricultural fields from the remote sensing images is a crucial task in digital and precision agriculture. Deep learning methods have the advantages of fast and accurate image segmentation, especially for extracting the agricultural fields from remote sensing images. This paper proposed a deep neural network with a dual attention mechanism and a multi-scale feature fusion (Dual Attention and Scale Fusion Network, DASFNet) to extract the cropland from a GaoFen-2 (GF-2) image of 2017 in Alar, south Xinjiang, China. First, we constructed an agricultural field segmentation dataset from the GF-2 image. Next, seven evaluation indices were selected to assess the extraction accuracy, including the location shift, to reveal the spatial relationship and facilitate a better evaluation. Finally, we proposed DASFNet incorporating three ameliorated and novel deep learning modules with the dual attention mechanism and multi-scale feature fusion methods. The comparison of these modules indicated their effects and advantages. Compared with different segmentation convolutional neural networks, DASFNet achieved the best testing accuracy in extracting fields with an F1-score of 0.9017, an intersection over a union of 0.8932, a Kappa coefficient of 0.8869, and a location shift of 1.1752 pixels. Agricultural fields can be extracted automatedly and accurately using DASFNet, which reduces the manual record of the agricultural field information and is conducive to further farmland surveys, protection, and management. [ABSTRACT FROM AUTHOR]

Under the dual stimulus of the new energy demand and the increasing competitiveness of wind energy, the construction of wind speed prediction models began to be placed in a position that cannot be ignored. To overcome the challenges brought by wind speed fluctuations to wind speed forecasting, this paper proposes a novel hybrid wind speed forecasting deep model. The model has three modules, including data preprocessing, multi-learner ensemble, and adaptive multiple error correction. We used four real wind series in Xinjiang, China to verify the performance of the model. The results of the case study show that: (a) The proposed hybrid deep model for wind speed forecasting is superior to several state-of-the-art models in terms of both forecasting stability and forecasting accuracy; (b) The proposed hybrid deep model is excellent in multi-step forecasting, taking the site #1 as an example, the MAEs of the proposed model are 0.0250 m/s, 0.0417 m/s, and 0.0570 m/s, respectively. • A hybrid framework based on deep learning is proposed for wind speed forecasting. • The data preprocessing method reduces the instability of wind speed data. • The multi-learner ensemble method integrates the advantages of deep learning models. • An adaptive multiple error correction method is developed. [ABSTRACT FROM AUTHOR]

[Display omitted] • Potential of spectral estimation of SOC was explored in the arid and semi-arid area. • Deep learning outperformed Random Forest in SOC prediction. • Combinational use of feature selection and deep learning can improve SOC prediction. • The sensitivity of various deep learning algorithms depended on the sample size. Soil organic carbon (SOC) plays an important role in soil functioning and also global C balance. Visible-near-infrared (Vis-NIR) spectroscopy can be regarded as a cost-effective alternative to monitor the SOC content. Previously, application of Vis-NIR spectroscopy in the quantitative estimation of SOC in arid and semi-arid regions has received relatively little attention. Here, three different sample sizes of dataset (i.e., 330, 660, and 990) with SOC contents and Vis-NIR spectroscopy measured in the laboratory were obtained from Southern Xinjiang, China. Eight feature selection methods, including Interval Random Frog (IRF), were used to extract the optimal spectral feature subset. Six deep learning (DL) algorithms (e.g., Long Short-Term Memory Neural Networks, LSTM; Deep Belief Networks, DBN) and one machine learning method (Random Forest, RF) were utilized to relate SOC to spectral predictors. The overall objective of this work was to compare the predicted potentials of seven modeling algorithms combined with eight feature selection methods for spectral prediction of SOC. In addition, this paper also investigated the influence of different calibration sample size on the final modeling accuracy for SOC. Results indicated that the DL algorithms outperformed RF for SOC prediction. Among the six DL approaches, the LSTM model performed the best, while the DBN model performed the worst. The one-dimensional-Convolutional Neural Network (1D-CNN), 2D-CNN, Recurrent Neural Network, and DBN algorithms were sensitive to different sample sizes. For the largest dataset (i.e., 990 samples), four of the eight feature selection methods combined with the DL algorithms could improve the prediction for SOC, relative to the corresponding full-spectrum DL models. Among all models developed for SOC, the IRF-LSTM model achieved the optimal prediction, with the validation R2 of 0.89. Our findings provided both theoretical and technical guidance for the spectral estimation of SOC with the relatively low values in arid and semi-arid area. [ABSTRACT FROM AUTHOR]

Remote sensing image segmentation with samples imbalance is always one of the most important issues. Typically, a high-resolution remote sensing image has the characteristics of high spatial resolution and low spectral resolution, complex large-scale land covers, small class differences for some land covers, vague foreground, and imbalanced distribution of samples. However, traditional machine learning algorithms have limitations in deep image feature extraction and dealing with sample imbalance issue. In the paper, we proposed an improved full-convolution neural network, called DeepLab V3+, with loss function based solution of samples imbalance. In addition, we select Sentinel-2 remote sensing images covering the Yuli County, Bayingolin Mongol Autonomous Prefecture, Xinjiang Uygur Autonomous Region, China as data sources, then a typical region image dataset is built by data augmentation. The experimental results show that the improved DeepLab V3+ model can not only utilize the spectral information of high-resolution remote sensing images, but also consider its rich spatial information. The classification accuracy of the proposed method on the test dataset reaches 97.97%. The mean Intersection-over-Union reaches 87.74%, and the Kappa coefficient 0.9587. The work provides methodological guidance to sample imbalance correction, and the established data resource can be a reference to further study in the future. [ABSTRACT FROM AUTHOR]